1. Volume 81, Issue 3, Pages 664-673 (February 2014)
Crossmodal Induction of Thalamocortical Potentiation Leads to Enhanced Information Processing in the Auditory Cortex 
Emily Petrus, Amal Isaiah, Adam P. Jones, David Li, Hui Wang, Hey-Kyoung Lee, Patrick O. Kanold 
Neuron 
Volume 81, Issue 3, Pages (February 2014)
DOI: /j.neuron
Copyright © 2014 Elsevier Inc. Terms and Conditions

2. Figure 1 Response Characteristics of A1 Neurons Change after DE
(A) Representative FRAs showing increase in firing rate as a function of intensity of sound. Occasionally a multipeaked response pattern (right) was observed.
(B–E) Characterization of response properties. The top rasters indicate measurements for an example cell. Black horizontal bar indicates duration of sound (40 ms). Spontaneous rate is measured in blue area. Significant responses were first identified using Victor’s binless method (Figure S1) for estimating the stimulus-related information in the spike trains (Victor, 2002). This algorithm searches, via a sliding window (green), for significant neuronal responses within user-set limits of a response window (600 ms following onset of stimulus) and compares the observed spike rates within that window to those seen within a chosen window deemed to contain only spontaneous activity (200 ms preceding onset of stimulus, blue), while treating the latter as a Poisson process. Here, the size of the sliding window is inversely proportional to the temporal precision of recording spike-related information. (B) First spikes in each trial are indicated in green (C). Peak and mean rates are measured within the response window (identified by the binless algorithm, green areas in D and E, respectively). Lower graphs show the distributions of the response properties between NR and DE cells (n = 173 and 175, respectively). Box plots indicate mean ± 95% confidence interval. ∗∗ and ∗ indicate p < 0.001and p < 0.05, respectively. ns, not significant. See also Figures S1 and S2.
Neuron  , DOI: ( /j.neuron )
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3. Figure 2 Tuning Characteristics of A1 Neurons Change after DE
(A) Representative rate-level functions for NR (upper) and DE (lower), respectively. Θ represents slope.
(B–D) Derivation of FRA-related statistics. (B) Mean evoked rate calculated from contours of spike rates at center frequency of the cell. (C) Thresholds calculated from lowest-sound-pressure level at which responses were evoked and (D) derivation of quality factor at 10 dB above (Q10) the threshold.
(E–H) Summary statistics of tuned units. NR and DE are identified by red and blue cumulative distribution functions, with mean and 95% confidence intervals shown in inset. (E) Slopes of rate-level functions. (F) Comparison of mean firing rates at characteristic frequencies (CF). (G) Thresholds. (H) Q10.
Neuron  , DOI: ( /j.neuron )
Copyright © 2014 Elsevier Inc. Terms and Conditions

4. Figure 3
Crossmodal Potentiation of TC Synapses in A1 without Changes in V1
(A) Crossmodal regulation of TC synapses in A1-L4. Top: AAV-ChR2-EYFP injection to MGB. Note expression of EYFP (green) in MGB (left and center panels). Top right: a biocytin-filled A1-L4 neuron (red) with DAPI (blue) and EYFP (green). Middle: Example traces of LEv-Sr2+-mEPSCs from NR, DE, and LE group. A 5 ms duration LED light was delivered at the arrowhead to activate TC synapses. Spontaneous events were collected during a 400 ms window (gray dotted line) before the LED, and LEv-Sr2+-mEPSCs were measured during a 400 ms window 50 ms after the LED (blue solid line). Bottom left: average calculated LEv-Sr2+-mEPSC amplitude of thalamocortical inputs (see Supplemental Experimental Procedures). ∗p < 0.04, ANOVA. Bottom right: average raw LEv-Sr2+-mEPSC traces (without subtracting spontaneous events).
(B) TC synapses in V1-L4. Top: AAV-ChR2-EYFP injection to LGN. Note EYFP (green) in LGN (left and center panels). Top right: A biocytin-filled V1-L4 neuron (red) with DAPI (blue) and YFP (green). Middle: Example traces of LEv-Sr2+-mEPSCs. Marks are the same as in (A). Bottom left: average calculated LEv-Sr2+-mEPSC amplitude of TC inputs. Bottom right: average raw LEv-Sr2+-mEPSC traces. See Table S1 for data. Bar graphs are mean ± SEM.
Neuron  , DOI: ( /j.neuron )
Copyright © 2014 Elsevier Inc. Terms and Conditions

5. Figure 4
Crossmodal Potentiation of A1 L4 mEPSCs Is Age-Independent and Nonmultiplicative
(A) Results from juvenile (P28) mice. DE increases the average mEPSC amplitude of A1-L4, which reverses with LE (bottom left). Top: average mEPSC traces. Bottom right: average mEPSC frequency.
(B) Results from adult (P90) mice. In A1-L4, DE increases the mEPSC amplitude, which reverses with LE (B, bottom left). Top: average mEPSC traces. Bottom right: average mEPSC frequency. ∗p < 0.05, ANOVA. See Table S2 for data.
(C) DE induces a nonmultiplicative increase in mEPSC amplitude of A1-L4 in young mice. The amplitudes of NR mEPSCs were multiplied by a scaling factor of 1.27 to match the average mEPSC amplitude to that of DE (Kolmogorov-Smirnov test between DE and NR scaled: p < ).
(D) Nonmultiplicative increase in mEPSC amplitude of A1-L4 in P90 mice with 7d-DE. Scaling factor was 1.17 (Kolmogorov-Smirnov test between DE and NR scaled: p < ).
(E) DE increases the average mEPSC amplitude of A1-L4 neurons of CBA mice, which do not undergo age-related hearing loss. Top: average mEPSC amplitude comparison. Bottom left: average mEPSC traces. Bottom right: No change in the average mEPSC frequency. ∗p < 0.02, t test. Bar graphs are mean ± SEM.
Neuron  , DOI: ( /j.neuron )
Copyright © 2014 Elsevier Inc. Terms and Conditions

6. Figure 5
Crossmodal Potentiation of TC Synapses Is Observed with Deafening and Is Experience Dependent
(A) Regulation of TC synapses in A1-L4. Top: Example traces of LEv-Sr2+-mEPSCs from NR, deaf (DF), and DE+DF (DD) group. A 5 ms duration LED light was delivered at the arrowhead to activate TC synapses. Marks are the same as in Figure 3. Bottom left: average calculated LEv-Sr2+-mEPSC amplitude of TC inputs (see Supplemental Experimental Procedures). Bottom right: average raw LEv-Sr2+-mEPSC traces (without subtracting spontaneous events).
(B) Crossmodal potentiation of TC synapses in V1-L4 after deafening. Top: example traces of LEv-Sr2+-mEPSCs. Marks are the same as in Figure 3. Bottom left: average calculated LEv-Sr2+-mEPSC amplitude of TC inputs. ∗p < 0.008, t test. Bottom right: average raw LEv-Sr2+-mEPSC traces. Bar graphs plot mean ± SEM. See Table S3 for data and Figure S4.
Neuron  , DOI: ( /j.neuron )
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7. Figure 6 Schematic of MDA Approach
(A) A principal components analysis was performed on the spike-count vectors forming the feature space obtained for each unit at each of the frequency-level combinations for n stimulus repetitions. The red and blue data are shown here projected along the first two principal components with a separation boundary between spike count vectors that are dissimilar to each other. In this case, the MDA estimates the probability with which each element of the stimulus matrix (21 frequencies × 7 levels) was assigned to the correct value based on the similarity between the observed responses to multiple repeats of the same stimulus. The centroid of each repeat was calculated, and every subsequent repeat was compared to the previous (random) repeat of the stimulus. Given that each population of spike counts from multiple sets of neuronal responses had that many degrees of freedom, we reduced the dimensionality to the first ten principal components, which accounted for ≥50% of the variance in the samples. Adjusted spike counts were calculated by subtracting the spontaneous rates and Z scored prior to classification to determine the reliability of responses over and above a simple increase in responsiveness alone. The performance of this classifier was evaluated by generating a confusion matrix that plots the known value of the stimulus on the x axis and the model-predicted identity of the stimulus on the y axis, with perfect classification performance indicated by the 45° diagonal and erroneous assignments made away from the diagonal. The mutual information (MI) between the true value of the stimulus and the predicted value was estimated to quantify how well the neurons encode different stimulus features (frequencies and levels).
(B) DE increases frequency discrimination of A1 neurons. Confusion matrices for NR (upper panel) and DE (lower panel) showing model-predicted frequency for each known frequency value. Each column represents discrimination performance at three different temporal resolutions (1, 10, and 50 ms; shown on top of each column). Color scale indicates proportion of classifications, and diagonal alignment indicates near-perfect classification performance (identical for all plots). The bias-corrected MI is indicated at the top of each figure. The most number of correct frequency assignments appeared to be made by the classifier closer to the groups’ overall CFs.
(C) DE increases level discrimination of A1 neurons. Confusion matrices for sound-level-based classification for NR (upper panel) and DE (lower panel). DE increased MI uniformly for both aspects of stimuli. The discriminability approaches saturation in performance beyond 60 dB SPL.
Neuron  , DOI: ( /j.neuron )
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8. Figure 7 DE Increases Spiking Reliability
(A) Variance of spike counts as function of mean spike counts at three different temporal resolutions. The ratio of variance and mean spike counts (Fano factor; shown as slope of regression fit) is decreased after DE consistent with MI comparisons.
(B and C) Fano factor (ratio of variance and mean spike counts) at three different temporal resolutions. Consistent with MI comparisons, Fano factor (FF) showed a significant overall decrease after DE when compared separately for frequencies (B) and sound levels (C; p < 0.05; t test). Plotted are means ± SD.
Neuron  , DOI: ( /j.neuron )
Copyright © 2014 Elsevier Inc. Terms and Conditions